At present, along with rapid development of the Internet, network virtual life becomes more and more important. Compared with true-life figures, a network avatar becomes an important presence mode in a personal network space. However, few people take a true-life photo as the network avatar in a network environment. Usually, they adopt a cartoon sketch to express his/her avatar instead. Currently, most sketches are developed by network content providers, and users choose from these sketches. This mode requires low costs and provides the users with free sketches as a value-added part of network services. However, along with an increase of the number of users, the sketches of different users are all most the same, which cannot satisfy the users' individualized requirements to the network sketches. Although a few users can invite artists to design individualized sketches for them, it costs much and cannot benefit all network users.
In order to enable a computer to generate sketches automatically, some technical schemes for generating sketches are provided. For example, Brennan in MIT presents an interactive caricature generating system in 1982; Japanese scholar Murakami et al. develops a template-based facial caricature system PICASSO and a web-based edition web-PICASSO. However, the above caricature generating schemes are mainly based on rules manually pre-defined, they can just generate sketches with limited variations, and thus makes the sketches unexpressive.
In order to generate more individualized sketches automatically, some improved technical schemes are provided currently. For example, Chinese “Software Transaction” discloses “an individualized cartoon system based on images” in issue 09, vol. 13, 2002; US patent publication No. US2003/0095701A1, US2005/0100243A1 and US2006/0082579A1 disclose similar techniques for generating sketches automatically. These techniques for automatically generating sketches adopt a sample-based learning policy, and adopt a non-parameter sampling method to model a complicated relationship between an image and a corresponding sketch, and thus generate an individualized sketch for any image according to the learned relationship.
However, a main defect of the above methods lies in that, they adopt short lines as basic geometric elements to construct a sketch. One sketch usually consists of many short lines and it is required to extract, analyze and compute for each short line. Therefore, computation load is large and processing efficiency is low. In addition, the above methods require a strict corresponding relationship between an original image and a sketch, and thus can only generate limited styles of sketches. Because the sketch is strictly in line with the original image, the sketch seems too rigid and it is difficult to change its style, e.g. to support an exaggerated style.